In the bin packing problem, the task is to select one or more bins from a set of available bins to pack three dimensional, rectangular boxes such that the usage of the bin space is maximized. Read more about the problem.

The package uses Genetic algorithm for the three-dimensional bin packing problem with heterogeneous bins.

You can read more about the algorithm in my blog post.

```
devtools::install_github('delta1epsilon/BoxPacking')
```

Consider an example where 20 boxes of different sizes are going to be packed into containers 2x2x2.

```
library(BoxPacking)
# create containers
containers <- list()
n_containers <- 4
for (i in 1:n_containers) {
containers <- c(containers,
Container(length = 2, height = 2, width = 2)
)
}
# create boxes
boxes <- list()
n_boxes <- 20
for (i in 1:n_boxes) {
length <- sample(c(0.4, 0.5, 1), 1)
height <- sample(c(0.4, 0.5, 1), 1)
width <- sample(c(0.4, 0.5, 1), 1)
boxes <- c(boxes,
Box(length = length, height = height, width = width)
)
}
# Box Packing
solution <-
PerformBoxPacking(containers = containers,
boxes = boxes,
n_iter = 4,
population_size = 20,
elitism_size = 5,
crossover_prob = 0.5,
mutation_prob = 0.5,
verbose = TRUE,
plotSolution = TRUE
)
```

delta1epsilon/BoxPacking documentation built on May 12, 2017, 10:49 p.m.

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